#!/usr/bin/env python3
"""
example.py — Usage demonstrations for hv_quorum_inference.

NumPy only.
"""

import numpy as np

from hv_quorum_inference import (
    QuorumConfig, quorum, majority_vote, dominance,
    pairwise_divergence, closest_to_centroid,
    SyntheticModel,
)


def demo_simple_use():
    print("=" * 72)
    print("Demo 1 — simplest usage")
    print("=" * 72)
    print()

    # Build a synthetic backend (in production, replace with your LLM wrapper)
    model = SyntheticModel(mode_collapse_rate=0.8, seed=0)
    prompt = model.make_prompt(concentration=2.0)

    cfg = QuorumConfig(
        n_samples=64,
        max_resamples=1,
        dominance_threshold=0.70,
    )

    # Run quorum
    r = quorum(prompt, model, cfg)
    print(f"  collapse flag:   {r.collapsed}")
    print(f"  candidate:       {r.candidate}")
    print(f"  dominance:       {r.final_dominance:.3f}")
    print(f"  divergence:      {r.final_divergence:.3f}")
    print(f"  samples used:    {r.n_samples_used}")
    print()

    # The correct answer, for reference
    print(f"  correct answer:  {prompt['correct']}")
    print()


def demo_clean_vs_collapsed():
    print("=" * 72)
    print("Demo 2 — clean model vs collapsed model")
    print("=" * 72)
    print()

    cfg = QuorumConfig(n_samples=64, max_resamples=1,
                        dominance_threshold=0.70)

    for label, rate in [('clean (0% collapse)', 0.0),
                         ('moderate (50%)', 0.5),
                         ('severe (80%)', 0.8)]:
        model = SyntheticModel(mode_collapse_rate=rate, seed=0)
        flag_count = 0
        correct_count = 0
        for _ in range(50):
            prompt = model.make_prompt(concentration=2.0)
            r = quorum(prompt, model, cfg)
            flag_count += int(r.collapsed)
            correct_count += int(model.is_correct(r.candidate, prompt))
        print(f"  {label}")
        print(f"    flagged:   {flag_count / 50:.2f}")
        print(f"    correct:   {correct_count / 50:.2f}")
        print()


def demo_threshold_effect():
    print("=" * 72)
    print("Demo 3 — how threshold affects flag rate")
    print("=" * 72)
    print()

    print(f"  {'threshold':>10s}  {'flag@0%':>8s}  {'flag@70%':>9s}")
    print("  " + "-" * 32)
    for th in [0.55, 0.60, 0.65, 0.70, 0.75, 0.80]:
        cfg = QuorumConfig(n_samples=64, max_resamples=1,
                            dominance_threshold=th)
        flags = {}
        for rate in [0.0, 0.70]:
            m = SyntheticModel(mode_collapse_rate=rate, seed=42)
            count = 0
            for _ in range(100):
                p = m.make_prompt(concentration=2.0)
                r = quorum(p, m, cfg)
                count += int(r.collapsed)
            flags[rate] = count / 100
        print(f"  {th:>10.2f}  {flags[0.0]:>8.2f}  {flags[0.70]:>9.2f}")
    print()
    print("  Lower threshold → more flags, more false positives.")
    print()


def demo_majority_vs_quorum():
    print("=" * 72)
    print("Demo 4 — majority vs quorum at 80% collapse")
    print("=" * 72)
    print()

    cfg = QuorumConfig(n_samples=64, max_resamples=1,
                        dominance_threshold=0.70)
    model = SyntheticModel(mode_collapse_rate=0.8, seed=0)
    prompt = model.make_prompt(concentration=2.0)

    # majority
    r_maj = majority_vote(prompt, model, n_samples=64)
    print(f"  Majority vote:")
    print(f"    candidate:  {r_maj.candidate}")
    print(f"    correct:    {model.is_correct(r_maj.candidate, prompt)}")
    print(f"    dominance:  {r_maj.final_dominance:.3f}")
    print()

    # quorum (reset RNG for fairness)
    model2 = SyntheticModel(mode_collapse_rate=0.8, seed=0)
    prompt2 = model2.make_prompt(concentration=2.0)
    r_q = quorum(prompt2, model2, cfg)
    print(f"  Quorum:")
    print(f"    candidate:  {r_q.candidate}")
    print(f"    collapsed:  {r_q.collapsed}")
    print(f"    dominance:  {r_q.final_dominance:.3f}")
    print()
    print(f"  Correct answer: {prompt['correct']}")
    print()


def demo_signal_components():
    print("=" * 72)
    print("Demo 5 — inspect the signal values")
    print("=" * 72)
    print()

    print(f"  {'scenario':<24s}  {'dominance':>10s}  {'divergence':>12s}")
    print("  " + "-" * 50)

    for label, rate in [('clean', 0.0), ('moderate', 0.4),
                         ('severe', 0.8)]:
        model = SyntheticModel(mode_collapse_rate=rate, seed=1)
        prompt = model.make_prompt(concentration=2.0)
        samples = model.sample(prompt, 64)
        emb = model.embed(samples)
        dom = dominance(samples)
        div = pairwise_divergence(emb)
        print(f"  {label:<24s}  {dom:>10.3f}  {div:>12.3f}")
    print()
    print("  Dominance rises with collapse; divergence falls.")
    print("  Either could be a detector. This release uses dominance.")
    print()


def demo_custom_backend():
    print("=" * 72)
    print("Demo 6 — using a custom backend")
    print("=" * 72)
    print()

    # A trivial custom backend that just wraps SyntheticModel
    class Wrapped:
        def __init__(self, model):
            self.model = model
        def sample(self, prompt, n):
            return self.model.sample(prompt, n)
        def embed(self, candidates):
            return self.model.embed(candidates)
        def is_correct(self, candidate, prompt):
            return self.model.is_correct(candidate, prompt)

    base = SyntheticModel(mode_collapse_rate=0.6, seed=7)
    backend = Wrapped(base)
    prompt = base.make_prompt(concentration=2.0)

    cfg = QuorumConfig(n_samples=64, max_resamples=1,
                        dominance_threshold=0.70)
    r = quorum(prompt, backend, cfg)
    print(f"  collapsed:  {r.collapsed}")
    print(f"  candidate:  {r.candidate}")
    print(f"  dominance:  {r.final_dominance:.3f}")
    print()
    print("  To use a real LLM, replace the Wrapped class with a")
    print("  HuggingFaceBackend or API wrapper. The quorum algorithm")
    print("  only calls sample() and embed().")
    print()


def main():
    demo_simple_use()
    demo_clean_vs_collapsed()
    demo_threshold_effect()
    demo_majority_vs_quorum()
    demo_signal_components()
    demo_custom_backend()


if __name__ == "__main__":
    main()